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A multi-agent based distributed energy management scheme for smart grid applications

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  • Radhakrishnan, Bharat Menon
  • Srinivasan, Dipti

Abstract

A multi-agent system based distributed EMS (energy management system) is proposed in this paper to perform optimal energy allocation and management for grids comprising of renewables, storage and distributed generation. The reliable and efficient operation of smart grids is slackened due to the presence of intermittent renewables. As the load demand and renewables are uncertain throughout the day, an energy management system is essential to ensure grid stability and achieve reductions in operation costs and CO2 emissions. The main objectives of the proposed algorithm is to maintain power balance in the system and to ensure long cycle life for storage units by controlling their SOC (state of charge). The proposed EMS scheme is tested and validated on a practical test system, which replicates a small-scale smart grid with a variety of distributed sources, storage devices, loads, power electronic converters, and SCADA (supervisory control and data acquisition) system. This system is also connected to the utility grid and the power exchange is controlled with the help of a battery system through a fuzzy based decision-making framework. The proposed algorithm is also extensively verified and tested using a series of sensitivity analyses and benchmarking with existing algorithms.

Suggested Citation

  • Radhakrishnan, Bharat Menon & Srinivasan, Dipti, 2016. "A multi-agent based distributed energy management scheme for smart grid applications," Energy, Elsevier, vol. 103(C), pages 192-204.
    • Handle: RePEc:eee:energy:v:103:y:2016:i:c:p:192-204
    DOI: 10.1016/j.energy.2016.02.117
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    Cited by:

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    3. Javier Rodríguez-García & David Ribó-Pérez & Carlos Álvarez-Bel & Elisa Peñalvo-López, 2019. "Novel Conceptual Architecture for the Next-Generation Electricity Markets to Enhance a Large Penetration of Renewable Energy," Energies, MDPI, vol. 12(13), pages 1-23, July.
    4. Bendato, Ilaria & Bonfiglio, Andrea & Brignone, Massimo & Delfino, Federico & Pampararo, Fabio & Procopio, Renato & Rossi, Mansueto, 2018. "Design criteria for the optimal sizing of integrated photovoltaic-storage systems," Energy, Elsevier, vol. 149(C), pages 505-515.
    5. Jalali, Mehdi & Zare, Kazem & Seyedi, Heresh, 2017. "Strategic decision-making of distribution network operator with multi-microgrids considering demand response program," Energy, Elsevier, vol. 141(C), pages 1059-1071.
    6. Kang, Wenfa & Chen, Minyou & Lai, Wei & Luo, Yanyu, 2021. "Distributed real-time power management for virtual energy storage systems using dynamic price," Energy, Elsevier, vol. 216(C).
    7. Abdi, Hamdi & Beigvand, Soheil Derafshi & Scala, Massimo La, 2017. "A review of optimal power flow studies applied to smart grids and microgrids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 71(C), pages 742-766.
    8. Bünning, Felix & Sangi, Roozbeh & Müller, Dirk, 2017. "A Modelica library for the agent-based control of building energy systems," Applied Energy, Elsevier, vol. 193(C), pages 52-59.
    9. B. Kavya Santhoshi & K. Mohana Sundaram & Sanjeevikumar Padmanaban & Jens Bo Holm-Nielsen & Prabhakaran K. K., 2019. "Critical Review of PV Grid-Tied Inverters," Energies, MDPI, vol. 12(10), pages 1-26, May.
    10. K. Habibul Kabir & Shafquat Yasar Aurko & Md. Saifur Rahman, 2021. "Smart Power Management in OIC Countries: A Critical Overview Using SWOT-AHP and Hybrid MCDM Analysis," Energies, MDPI, vol. 14(20), pages 1-50, October.
    11. Reis, Inês F.G. & Gonçalves, Ivo & Lopes, Marta A.R. & Antunes, Carlos Henggeler, 2020. "A multi-agent system approach to exploit demand-side flexibility in an energy community," Utilities Policy, Elsevier, vol. 67(C).
    12. Hussain, Syed Asad & Razi, Faran & Hewage, Kasun & Sadiq, Rehan, 2023. "The perspective of energy poverty and 1st energy crisis of green transition," Energy, Elsevier, vol. 275(C).
    13. Coppitters, Diederik & De Paepe, Ward & Contino, Francesco, 2021. "Robust design optimization of a photovoltaic-battery-heat pump system with thermal storage under aleatory and epistemic uncertainty," Energy, Elsevier, vol. 229(C).
    14. Thomas, Dimitrios & Deblecker, Olivier & Ioakimidis, Christos S., 2016. "Optimal design and techno-economic analysis of an autonomous small isolated microgrid aiming at high RES penetration," Energy, Elsevier, vol. 116(P1), pages 364-379.
    15. Inês F. G. Reis & Ivo Gonçalves & Marta A. R. Lopes & Carlos Henggeler Antunes, 2021. "Assessing the Influence of Different Goals in Energy Communities’ Self-Sufficiency—An Optimized Multiagent Approach," Energies, MDPI, vol. 14(4), pages 1-32, February.
    16. Zhongda Lu & Qilong Wang & Fengxia Xu & Mingqing Fan & Chuanshui Peng & Shiwei Yan, 2023. "Double-Layer SOC and SOH Equalization Scheme for LiFePO 4 Battery Energy Storage System Using MAS Blackboard System," Energies, MDPI, vol. 16(14), pages 1-14, July.
    17. Adefarati, T. & Bansal, R.C., 2017. "Reliability assessment of distribution system with the integration of renewable distributed generation," Applied Energy, Elsevier, vol. 185(P1), pages 158-171.
    18. Nikmehr, Nima & Najafi-Ravadanegh, Sajad & Khodaei, Amin, 2017. "Probabilistic optimal scheduling of networked microgrids considering time-based demand response programs under uncertainty," Applied Energy, Elsevier, vol. 198(C), pages 267-279.
    19. Luis Hernández-Callejo, 2019. "A Comprehensive Review of Operation and Control, Maintenance and Lifespan Management, Grid Planning and Design, and Metering in Smart Grids," Energies, MDPI, vol. 12(9), pages 1-50, April.

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